arXiv:2606.24062v1 Announce Type: cross Abstract: Financial time series forecasting presents structural challenges absent from standard benchmarks. Log-returns are non-stationary, exhibit exceptionally low signal-to-noise (SNR) ratios, and are governed by regime-dependent temporal dependencies. We identify a key limitation of state-of-the-art (SOTA) time series models in financial settings. A fixed context window is mismatched to the time-varying optimal look-back of non-stationary price processes. We propose the Regime-Aware Variable-context Expert Network (RAVEN), a Mixture-of-Experts framew
The paper addresses a fundamental limitation in applying state-of-the-art time series models to financial data by proposing a novel architecture designed to handle non-stationarity and regime-dependent dependencies.
This research provides a significant step towards more accurate and robust financial forecasting, which is critical for investment strategies, risk management, and market stability.
The existing approach of fixed context windows in time series models for finance is challenged, opening the door for more adaptive, regime-aware models to become the new standard.
- · Quantitative hedge funds
- · High-frequency trading firms
- · Financial AI/ML researchers
- · Proprietary trading desks
- · Traditional time series models
- · Fixed-context forecasting methods
Increased accuracy and profitability for trading strategies employing RAVEN-like models.
Heightened competition in quantitative finance as new sophisticated models become more accessible or widely adopted.
Potential for an arms race in financial AI leading to accelerated market dynamics and new forms of stability or instability.
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Read at arXiv cs.AI